全局竞争和声搜索算法

提出一种全局竞争和声搜索(GCHS) 算法, 给出随机局部平均和声和全局平均和声的概念, 建立竞争搜索机制, 实现每次迭代产生两个和声向量并进行竞争选择. 设计自适应全局调整和局部学习策略, 平衡算法的局部搜索和全局搜索, 详细分析参数HMS、HMCR和PAR对算法优化性能的影响. 数值结果表明, GCHS 算法在精度、收敛速度和鲁棒性方面比和声搜索算法及最近文献中提出的7 种优秀改进和声搜索算法要好.

Global competitive harmony search algorithm

A global competitive harmony search algorithm(GCHS) is proposed. In this algorithm, the conceptions of stochastic local mean and global mean are given. The competition search mechanism is built to realize two harmony vectors are competition selection, and the two harmony vectors are both generated in the each iteration. The adaptive global pitch adjustment and local learning strategy are designed to balance the global search and local search. The effects that the parameter HMS, HMCR and PAR have on the performance of the GCHS algorithm are also analyzed in detail. The numerical results show the superiority of the proposed GCHS algorithm in terms of accuracy, convergence speed, and robustness when compared with the harmony search algorithm and other seven state-of-the-art harmony search algorithms.